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researcher

Umberto M. Tomasini

EPFL

8 papers hereh-index 6151 citations9 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author4
  • middle author3

Across the 7 of 8 papers where every author was matched, so the position is known.

fields
  • cs.LG3
  • cs.CL2
  • cs.AI1
  • nlin.CD1
  • stat.ML1
affiliations
  • EPFL

identity via Semantic Scholar / OpenAlex

activity
20202026
most citedHow Deep Neural Networks Learn Compositional Data: The Random Hierarchy Model

21 citations · 28 across the 8 of their papers we have counts for

collaborators
Showing stat.MLShow all

1 paper · 1 filter

stat.ML2024★ 2 cited

How Deep Networks Learn Sparse and Hierarchical Data: the Sparse Random Hierarchy Model

Umberto Tomasini, Matthieu Wyart

Understanding what makes high-dimensional data learnable is a fundamental question in machine learning. On the one hand, it is believed that the success of deep learning lies in it…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.